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A Study of Predicting Ability in State-Action Pair Prediction: Adaptability to an Almost-Periodic Disturbance

A Study of Predicting Ability in State-Action Pair Prediction: Adaptability to an Almost-Periodic Disturbance

Masashi Sugimoto, Naoya Iwamoto, Robert W. Johnston, Keizo Kanazawa, Yukinori Misaki, Kentarou Kurashige
Copyright: © 2017 |Volume: 7 |Issue: 1 |Pages: 15
ISSN: 1947-3087|EISSN: 1947-3079|EISBN13: 9781522513674|DOI: 10.4018/IJALR.2017010104
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MLA

Sugimoto, Masashi, et al. "A Study of Predicting Ability in State-Action Pair Prediction: Adaptability to an Almost-Periodic Disturbance." IJALR vol.7, no.1 2017: pp.52-66. http://doi.org/10.4018/IJALR.2017010104

APA

Sugimoto, M., Iwamoto, N., Johnston, R. W., Kanazawa, K., Misaki, Y., & Kurashige, K. (2017). A Study of Predicting Ability in State-Action Pair Prediction: Adaptability to an Almost-Periodic Disturbance. International Journal of Artificial Life Research (IJALR), 7(1), 52-66. http://doi.org/10.4018/IJALR.2017010104

Chicago

Sugimoto, Masashi, et al. "A Study of Predicting Ability in State-Action Pair Prediction: Adaptability to an Almost-Periodic Disturbance," International Journal of Artificial Life Research (IJALR) 7, no.1: 52-66. http://doi.org/10.4018/IJALR.2017010104

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Abstract

When a robot considers an action-decision based on a future prediction, it is necessary to know the property of disturbance signals from the outside environment. On the other hand, the properties of disturbance signals cannot be described simply, such as non-periodic function, nonlinear time-varying function nor almost-periodic function. In case of a robot control, sampling rate for control will be affected description of disturbance signals such as frequency or amplitude. If the sampling rate for acquiring a disturbance signal is not correct, the action will be taken far from its actual property. In general, future prediction using machine learning is based on the tendency obtained through past training or learning. In this case, an optimal action will be determined uniquely based on a property of disturbance. However, in this type of situation, the learning time increases in proportional to the amount of training data, either, the tendency may not be found using prediction, in the worst case. In this paper, we focus on prediction for almost-periodic disturbance. In particular, we consider the situation where almost-periodic disturbance signals occur. From this perspective, we propose a method that identifies the frequency of an almost- periodic function based on the frequency of the disturbance using Fourier transform, nearest-neighbor one-step-ahead forecasts and Nyquist-Shannon sampling theorem.

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